def __init__(
self,
network=None,
parcellation=None,
centroids=None,
):
# ============== Create graph ================
nx.classes.graph.Graph.__init__(self)
if network is not None:
if isinstance(network, nx.classes.graph.Graph):
# Copy graph
self.__dict__.update(network.__dict__)
else:
# Create weighted graph from a dataframe or
# numpy array
if isinstance(network, pd.DataFrame):
M = network.values
elif isinstance(network, np.ndarray):
M = network
network = weighted_graph_from_matrix(M, create_using=self)
# ===== Give anatomical labels to nodes ======
if parcellation is not None:
# assign parcellation names to nodes
self.set_parcellation(parcellation)
if centroids is not None:
# assign centroids to nodes
self.set_centroids(centroids)
# Tell BrainNetwork class which attributes to consider "anatomical"
# and therefore preserve when copying or creating new graphs
self.anatomical_node_attributes = anatomical_node_attributes()
self.anatomical_graph_attributes = anatomical_graph_attributes()
# intialise global measures as an empty dict
self.graph['global_measures'] = {}
def test_scale_weights():
G1 = mkg.scale_weights(simple_weighted_graph())
G2 = mkg.weighted_graph_from_matrix(symmetric_matrix_1() * -1)
assert nx.is_isomorphic(G1, G2, edge_match=em())
